In [6]:
aux_plt = az.plot_khat(res, show_bins=True, figsize=(7,5))
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mplt
import seaborn as sns
import arviz as az
import datetime
import fit_arima
import open_data
df_temp = open_data.open()
df = df_temp[0]
ritorno = fit_arima.compute(2, 1, catene=4, samples_per_chain=2500, burnin=1000)
19:07:23 - cmdstanpy - INFO - CmdStan start processing
chain 1 | | 00:00 Status
chain 2 | | 00:00 Status
chain 3 | | 00:00 Status
chain 4 | | 00:00 Status
19:29:26 - cmdstanpy - INFO - CmdStan done processing. 19:29:26 - cmdstanpy - WARNING - Non-fatal error during sampling: Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/home/br1/PythonProjects/PythonStats/git_bayesian_copie/ARIMA/Spatial/code.stan', line 177, column 4 to column 34) Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/home/br1/PythonProjects/PythonStats/git_bayesian_copie/ARIMA/Spatial/code.stan', line 177, column 4 to column 34) Exception: normal_lpdf: Scale parameter is 0, but must be positive! (in '/home/br1/PythonProjects/PythonStats/git_bayesian_copie/ARIMA/Spatial/code.stan', line 177, column 4 to column 34) Exception: code_model_namespace::log_prob: phi[1][1] is -nan, but must be greater than or equal to -1.000000 (in '/home/br1/PythonProjects/PythonStats/git_bayesian_copie/ARIMA/Spatial/code.stan', line 101, column 2 to column 47) Exception: code_model_namespace::log_prob: H is not symmetric. H[1,2] = inf, but H[2,1] = inf (in '/home/br1/PythonProjects/PythonStats/git_bayesian_copie/ARIMA/Spatial/code.stan', line 91, column 2 to column 53) Exception: code_model_namespace::log_prob: H is not symmetric. H[1,2] = inf, but H[2,1] = inf (in '/home/br1/PythonProjects/PythonStats/git_bayesian_copie/ARIMA/Spatial/code.stan', line 91, column 2 to column 53) Exception: code_model_namespace::log_prob: H is not symmetric. H[1,2] = inf, but H[2,1] = inf (in '/home/br1/PythonProjects/PythonStats/git_bayesian_copie/ARIMA/Spatial/code.stan', line 91, column 2 to column 53) Exception: code_model_namespace::log_prob: H is not symmetric. H[1,2] = inf, but H[2,1] = inf (in '/home/br1/PythonProjects/PythonStats/git_bayesian_copie/ARIMA/Spatial/code.stan', line 91, column 2 to column 53) Exception: code_model_namespace::log_prob: H is not symmetric. H[1,2] = inf, but H[2,1] = inf (in '/home/br1/PythonProjects/PythonStats/git_bayesian_copie/ARIMA/Spatial/code.stan', line 91, column 2 to column 53) Exception: code_model_namespace::log_prob: H is not symmetric. H[1,2] = inf, but H[2,1] = inf (in '/home/br1/PythonProjects/PythonStats/git_bayesian_copie/ARIMA/Spatial/code.stan', line 91, column 2 to column 53) Exception: code_model_namespace::log_prob: H is not symmetric. H[1,2] = inf, but H[2,1] = inf (in '/home/br1/PythonProjects/PythonStats/git_bayesian_copie/ARIMA/Spatial/code.stan', line 91, column 2 to column 53) Exception: code_model_namespace::log_prob: H is not symmetric. H[1,2] = inf, but H[2,1] = inf (in '/home/br1/PythonProjects/PythonStats/git_bayesian_copie/ARIMA/Spatial/code.stan', line 91, column 2 to column 53) Exception: code_model_namespace::log_prob: H is not symmetric. H[1,2] = inf, but H[2,1] = inf (in '/home/br1/PythonProjects/PythonStats/git_bayesian_copie/ARIMA/Spatial/code.stan', line 91, column 2 to column 53) Exception: code_model_namespace::log_prob: H is not symmetric. H[1,2] = inf, but H[2,1] = inf (in '/home/br1/PythonProjects/PythonStats/git_bayesian_copie/ARIMA/Spatial/code.stan', line 91, column 2 to column 53) Exception: code_model_namespace::log_prob: H is not symmetric. H[1,2] = inf, but H[2,1] = inf (in '/home/br1/PythonProjects/PythonStats/git_bayesian_copie/ARIMA/Spatial/code.stan', line 91, column 2 to column 53) Exception: code_model_namespace::log_prob: H is not symmetric. H[1,2] = inf, but H[2,1] = inf (in '/home/br1/PythonProjects/PythonStats/git_bayesian_copie/ARIMA/Spatial/code.stan', line 91, column 2 to column 53) Exception: code_model_namespace::log_prob: H is not symmetric. H[1,2] = inf, but H[2,1] = inf (in '/home/br1/PythonProjects/PythonStats/git_bayesian_copie/ARIMA/Spatial/code.stan', line 91, column 2 to column 53) Exception: code_model_namespace::log_prob: H is not symmetric. H[1,2] = inf, but H[2,1] = inf (in '/home/br1/PythonProjects/PythonStats/git_bayesian_copie/ARIMA/Spatial/code.stan', line 91, column 2 to column 53) Exception: code_model_namespace::log_prob: H is not symmetric. H[1,2] = inf, but H[2,1] = inf (in '/home/br1/PythonProjects/PythonStats/git_bayesian_copie/ARIMA/Spatial/code.stan', line 91, column 2 to column 53) Exception: code_model_namespace::log_prob: H is not symmetric. H[1,2] = inf, but H[2,1] = inf (in '/home/br1/PythonProjects/PythonStats/git_bayesian_copie/ARIMA/Spatial/code.stan', line 91, column 2 to column 53) Consider re-running with show_console=True if the above output is unclear!
/home/br1/PythonProjects/PythonStats/ARIMA_last_computation/Spatial/fit_arima.py:43: FutureWarning: Indexing with a float is deprecated, and will raise an IndexError in pandas 2.0. You can manually convert to an integer key instead.
y_missing_list.append({'Stazione':df.columns[v[i]],'Data':df.index[u[i]],'Samples':inference_data.posterior.w.values[:,:,i].reshape(catene*samples_per_chain)})
Tempo di computazione: 22:02
res = az.loo(ritorno['inference_data'], var_name="log_lik", pointwise=True)
res
/home/br1/PythonProjects/PythonStats/.venv/lib/python3.10/site-packages/arviz/stats/stats.py:803: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations. warnings.warn(
Computed from 10000 posterior samples and 17191 observations log-likelihood matrix.
Estimate SE
elpd_loo 7249.97 154.92
p_loo 287.49 -
There has been a warning during the calculation. Please check the results.
------
Pareto k diagnostic values:
Count Pct.
(-Inf, 0.5] (good) 17091 99.4%
(0.5, 0.7] (ok) 86 0.5%
(0.7, 1] (bad) 14 0.1%
(1, Inf) (very bad) 0 0.0%
aux_plt = az.plot_khat(res, show_bins=True, figsize=(7,5))
res = az.waic(ritorno['inference_data'], var_name="log_lik")
res
/home/br1/PythonProjects/PythonStats/.venv/lib/python3.10/site-packages/arviz/stats/stats.py:1645: UserWarning: For one or more samples the posterior variance of the log predictive densities exceeds 0.4. This could be indication of WAIC starting to fail. See http://arxiv.org/abs/1507.04544 for details warnings.warn(
Computed from 10000 posterior samples and 17191 observations log-likelihood matrix.
Estimate SE
elpd_waic 7262.00 154.79
p_waic 275.45 -
There has been a warning during the calculation. Please check the results.
plt_aux = az.plot_trace(ritorno['inference_data'], var_names=['phi','theta','hyper_gamma_phi_m','hyper_gamma_phi_s'], divergences=True, figsize=(15,15))
plt_aux = az.plot_trace(ritorno['inference_data'], var_names=['mh_sigma','sh_sigma','sigma'], divergences=True, figsize=(15,10))
plt_aux = az.plot_trace(ritorno['inference_data'], var_names=['alpha','w_s','betas','c'], divergences=True, figsize=(15,15))
df_risultati = az.summary(ritorno['inference_data'], var_names=['betas'], hdi_prob=0.95)
df_risultati.loc[:,'Name'] = ['Altitude','Type_Suburban','Type_Rural','Zoning_2','Zoning_3'] #????????? Come sono state fatte le covariate?
df_risultati.set_index('Name', inplace=True)
df_risultati
| mean | sd | hdi_2.5% | hdi_97.5% | mcse_mean | mcse_sd | ess_bulk | ess_tail | r_hat | |
|---|---|---|---|---|---|---|---|---|---|
| Name | |||||||||
| Altitude | -0.093 | 0.016 | -0.126 | -0.063 | 0.000 | 0.000 | 3364.0 | 4784.0 | 1.0 |
| Type_Suburban | 0.047 | 0.021 | 0.005 | 0.089 | 0.000 | 0.000 | 7476.0 | 7287.0 | 1.0 |
| Type_Rural | 0.047 | 0.050 | -0.051 | 0.144 | 0.001 | 0.001 | 2872.0 | 4449.0 | 1.0 |
| Zoning_2 | -0.007 | 0.027 | -0.058 | 0.046 | 0.001 | 0.001 | 948.0 | 2215.0 | 1.0 |
| Zoning_3 | -0.007 | 0.027 | -0.060 | 0.045 | 0.001 | 0.001 | 1009.0 | 2075.0 | 1.0 |
import time
from matplotlib.patches import Rectangle
plt.figure(figsize=(10,3))
ax = plt.subplot(1,1,1)
plt.xlim(left = -0.3, right = 0.3)
pos_rows = np.arange(0,len(df_risultati.index))
plt.ylim(bottom=-0.75, top=pos_rows[-1]+1.0)
i = 1
for row in df_risultati.index:
lci = df_risultati.loc[row, 'hdi_2.5%']
rci = df_risultati.loc[row, 'hdi_97.5%']
len_ci = rci - lci
if lci*rci <= 0:
col = 'red'
else:
col = 'blue'
ax.add_patch(Rectangle((lci, pos_rows[-i] - 0.75/2), len_ci, 0.75,
edgecolor = 'grey',
facecolor = col,
fill=True,
lw=1))
i += 1
plt.axvline(0, 0, pos_rows[-1]+0.75, linestyle='--', color='black')
plt.grid(axis='both')
plt.yticks(ticks=np.flip(pos_rows), labels=df_risultati.index.to_list())
plt.xticks(ticks=np.linspace(-0.3,0.3,13))
plt.show()
aux_shape = ritorno['inference_data'].posterior.gamma_th.values.shape
sns.kdeplot(ritorno['inference_data'].posterior.gamma_th.values.reshape((aux_shape[0]*aux_shape[1])), legend=False)
<AxesSubplot: ylabel='Density'>
aux_shape = ritorno['inference_data'].posterior.phi.values.shape
sns.kdeplot(ritorno['inference_data'].posterior.phi.values.reshape((aux_shape[0]*aux_shape[1],aux_shape[2],aux_shape[3]))[:,0,:], legend=False)
<AxesSubplot: ylabel='Density'>
aux_shape = ritorno['inference_data'].posterior.phi.values.shape
sns.kdeplot(ritorno['inference_data'].posterior.phi.values.reshape((aux_shape[0]*aux_shape[1],aux_shape[2],aux_shape[3]))[:,1,:], legend=False)
<AxesSubplot: ylabel='Density'>
aux_shape = ritorno['inference_data'].posterior.c.values.shape
sns.kdeplot(ritorno['inference_data'].posterior.c.values.reshape((aux_shape[0]*aux_shape[1],aux_shape[2])), legend=False)
<AxesSubplot: ylabel='Density'>
aux_shape = ritorno['inference_data'].posterior.w_s.values.shape
sns.kdeplot(ritorno['inference_data'].posterior.w_s.values.reshape((aux_shape[0]*aux_shape[1],aux_shape[2])), legend=False)
<AxesSubplot: ylabel='Density'>
pd.set_option('display.max_columns', 500)
spatial_results = pd.DataFrame(ritorno['inference_data'].posterior.w_s.values.reshape((aux_shape[0]*aux_shape[1],aux_shape[2])), columns=df.columns).describe()
spatial_results.loc['abs_dist_from_mean',:] = (spatial_results.loc['mean',:] - spatial_results.loc['mean',:].mean()).abs()
spatial_results.sort_values('abs_dist_from_mean', axis=1, ascending=False)
#spatial_results.sort_values('mean', axis=1, ascending=True)
| SAVIGNANO DI RIGO | TIMAVO | SAN FRANCESCO | PARCO EDILCARANI | VERUCCHIO | CORTE BRUGNATELLA | CITTADELLA | GERBIDO | MONTEBELLO | MARECCHIA | DE AMICIS | GIARDINI MARGHERITA | S. LAZZARO | GIARDINI | FEBBIO | SAN LAZZARO | S. ROCCO | SAN PIETRO CAPOFIUME | PARCO FERRARI | CASTELLARANO | SARAGAT | CENO | CASTELLUCCIO | PARCO RESISTENZA | PARCO MONTECUCCO | PORTA SAN FELICE | BADIA | PARCO BERTOZZI | ROMA | GIORDANI-FARNESE | ZALAMELLA | REMESINA | LUGAGNANO | FLAMINIA | DELTA CERVIA | FRANCHINI-ANGELONI | PARADIGNA | SAVIGNANO | GAVELLO | SAN LEO | BESENZONE | MALCANTONE | GHERARDI | CENTO | VILLA FULVIA | CAORLE | BOGOLESE | ISONZO | VIA CHIARINI | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 |
| mean | -0.306410 | 0.102571 | 0.093294 | 0.090832 | -0.080935 | -0.076943 | 0.077313 | 0.076855 | 0.075405 | -0.065292 | -0.062066 | -0.061775 | 0.069143 | 0.063326 | 0.061142 | -0.051587 | 0.060237 | -0.049831 | 0.058399 | 0.056898 | 0.056483 | 0.055782 | -0.045742 | -0.037341 | 0.045815 | -0.036549 | -0.036031 | -0.035278 | -0.034291 | 0.042434 | -0.028845 | 0.036386 | 0.036101 | 0.034391 | -0.012834 | -0.012742 | 0.021605 | -0.011015 | -0.008718 | -0.008249 | 0.015842 | 0.015607 | -0.005671 | 0.012018 | 0.011800 | 0.009843 | 0.001343 | 0.002770 | 0.004920 |
| std | 0.038593 | 0.039460 | 0.032450 | 0.032496 | 0.034201 | 0.050646 | 0.031196 | 0.057665 | 0.030105 | 0.035118 | 0.034504 | 0.036763 | 0.037855 | 0.036776 | 0.068578 | 0.040230 | 0.031529 | 0.036093 | 0.036843 | 0.037665 | 0.033729 | 0.047916 | 0.060295 | 0.039012 | 0.036722 | 0.033188 | 0.031056 | 0.041860 | 0.039133 | 0.034448 | 0.035586 | 0.039290 | 0.041153 | 0.040622 | 0.031650 | 0.040524 | 0.054844 | 0.039552 | 0.031315 | 0.042009 | 0.033783 | 0.055393 | 0.033313 | 0.041055 | 0.039079 | 0.037329 | 0.055658 | 0.039286 | 0.037974 |
| min | -0.451929 | -0.028114 | -0.021401 | -0.032652 | -0.211771 | -0.268127 | -0.041326 | -0.134485 | -0.035171 | -0.202785 | -0.214657 | -0.215806 | -0.076860 | -0.097305 | -0.210157 | -0.213683 | -0.079157 | -0.217263 | -0.085559 | -0.080720 | -0.071879 | -0.109718 | -0.285971 | -0.185684 | -0.090075 | -0.176187 | -0.161792 | -0.205623 | -0.196196 | -0.096995 | -0.153849 | -0.122254 | -0.132079 | -0.120896 | -0.129597 | -0.178752 | -0.196225 | -0.161476 | -0.128605 | -0.172774 | -0.119853 | -0.230863 | -0.130816 | -0.169802 | -0.148996 | -0.121303 | -0.242722 | -0.166949 | -0.148257 |
| 25% | -0.332056 | 0.076158 | 0.071530 | 0.068725 | -0.103766 | -0.111479 | 0.056758 | 0.037728 | 0.055765 | -0.088718 | -0.085327 | -0.085926 | 0.043638 | 0.039017 | 0.014856 | -0.078338 | 0.039341 | -0.074185 | 0.033950 | 0.031700 | 0.034359 | 0.023779 | -0.086205 | -0.063159 | 0.021231 | -0.058919 | -0.057233 | -0.062632 | -0.060673 | 0.019139 | -0.052461 | 0.010261 | 0.009016 | 0.007730 | -0.034144 | -0.039517 | -0.014436 | -0.037891 | -0.029307 | -0.036153 | -0.006299 | -0.021360 | -0.027651 | -0.015379 | -0.013569 | -0.015560 | -0.036027 | -0.023254 | -0.020344 |
| 50% | -0.306119 | 0.102565 | 0.093290 | 0.090919 | -0.080592 | -0.077135 | 0.077146 | 0.075648 | 0.074875 | -0.065298 | -0.061733 | -0.061719 | 0.068317 | 0.062872 | 0.061203 | -0.051260 | 0.060492 | -0.049933 | 0.058608 | 0.056537 | 0.056151 | 0.055101 | -0.046483 | -0.037723 | 0.045985 | -0.036443 | -0.036442 | -0.035396 | -0.033986 | 0.042640 | -0.028935 | 0.035967 | 0.036768 | 0.034257 | -0.013034 | -0.012768 | 0.021783 | -0.011417 | -0.008691 | -0.007306 | 0.016452 | 0.014788 | -0.006140 | 0.011774 | 0.012006 | 0.010301 | 0.001695 | 0.002734 | 0.004676 |
| 75% | -0.280899 | 0.128534 | 0.114788 | 0.112575 | -0.058193 | -0.043456 | 0.097972 | 0.114672 | 0.095279 | -0.042370 | -0.038801 | -0.037511 | 0.094824 | 0.087648 | 0.106811 | -0.024475 | 0.081286 | -0.025785 | 0.083098 | 0.082371 | 0.078875 | 0.087060 | -0.006075 | -0.011370 | 0.070106 | -0.014464 | -0.015421 | -0.007505 | -0.008064 | 0.065347 | -0.004887 | 0.062472 | 0.063716 | 0.061462 | 0.008292 | 0.014096 | 0.056994 | 0.015043 | 0.011688 | 0.019610 | 0.037919 | 0.052351 | 0.016458 | 0.039743 | 0.037228 | 0.034770 | 0.038052 | 0.029785 | 0.030628 |
| max | -0.155581 | 0.257271 | 0.224467 | 0.214644 | 0.044538 | 0.118328 | 0.192406 | 0.326672 | 0.187755 | 0.074742 | 0.106161 | 0.074917 | 0.218612 | 0.211243 | 0.332445 | 0.100380 | 0.202812 | 0.125044 | 0.208785 | 0.190513 | 0.182835 | 0.263622 | 0.173644 | 0.139213 | 0.225210 | 0.111891 | 0.115185 | 0.120552 | 0.114773 | 0.166962 | 0.100064 | 0.200000 | 0.214804 | 0.231678 | 0.114608 | 0.172252 | 0.280677 | 0.133076 | 0.098604 | 0.170170 | 0.180741 | 0.246557 | 0.146209 | 0.170174 | 0.146710 | 0.156652 | 0.236301 | 0.149952 | 0.157490 |
| abs_dist_from_mean | 0.310908 | 0.098073 | 0.088796 | 0.086334 | 0.085434 | 0.081441 | 0.072815 | 0.072357 | 0.070906 | 0.069790 | 0.066564 | 0.066273 | 0.064645 | 0.058828 | 0.056644 | 0.056086 | 0.055739 | 0.054329 | 0.053901 | 0.052399 | 0.051984 | 0.051284 | 0.050240 | 0.041839 | 0.041317 | 0.041048 | 0.040529 | 0.039776 | 0.038789 | 0.037935 | 0.033343 | 0.031888 | 0.031603 | 0.029893 | 0.017333 | 0.017241 | 0.017107 | 0.015513 | 0.013216 | 0.012747 | 0.011343 | 0.011109 | 0.010169 | 0.007520 | 0.007302 | 0.005345 | 0.003155 | 0.001728 | 0.000422 |
aux_shape = ritorno['inference_data'].posterior.annual_mean.values.shape
sns.kdeplot(ritorno['inference_data'].posterior.annual_mean.values.reshape((aux_shape[0]*aux_shape[1],aux_shape[2])), legend=False)
<AxesSubplot: ylabel='Density'>
aux_shape = ritorno['inference_data'].posterior.annual_median.values.shape
sns.kdeplot(ritorno['inference_data'].posterior.annual_median.values.reshape((aux_shape[0]*aux_shape[1],aux_shape[2])), legend=False)
<AxesSubplot: ylabel='Density'>
aux_shape = ritorno['inference_data'].posterior.annual_max.values.shape
sns.kdeplot(ritorno['inference_data'].posterior.annual_max.values.reshape((aux_shape[0]*aux_shape[1],aux_shape[2])), legend=False)
<AxesSubplot: ylabel='Density'>
valori_31_dic = []
for dizionario in ritorno['missing']:
if dizionario['Data'] == '2018-12-31':
valori_31_dic.append(dizionario['Samples'])
res_plot = sns.kdeplot(valori_31_dic, legend=False)
aux_shape = ritorno['inference_data'].posterior.annual_days_over_threshold.values.shape
sns.kdeplot(ritorno['inference_data'].posterior.annual_days_over_threshold.values.reshape((aux_shape[0]*aux_shape[1],aux_shape[2])), legend=False)
<AxesSubplot: ylabel='Density'>
pd.set_option('display.max_columns', 500)
aux_results = pd.DataFrame(ritorno['inference_data'].posterior.annual_days_over_threshold.values.reshape((aux_shape[0]*aux_shape[1],aux_shape[2])), columns=df.columns).describe()
aux_results.sort_values('mean', axis=1, ascending=True)
| CASTELLUCCIO | CORTE BRUGNATELLA | SAVIGNANO DI RIGO | FEBBIO | SAN LEO | VERUCCHIO | BADIA | GIARDINI MARGHERITA | MARECCHIA | SAN PIETRO CAPOFIUME | LUGAGNANO | DELTA CERVIA | PARCO BERTOZZI | SAN LAZZARO | PARCO RESISTENZA | GHERARDI | FRANCHINI-ANGELONI | VIA CHIARINI | BESENZONE | DE AMICIS | PORTA SAN FELICE | GAVELLO | SAVIGNANO | CAORLE | VILLA FULVIA | CASTELLARANO | ROMA | PARCO MONTECUCCO | CENTO | BOGOLESE | ZALAMELLA | SAN FRANCESCO | MALCANTONE | REMESINA | SARAGAT | PARADIGNA | S. LAZZARO | S. ROCCO | CITTADELLA | PARCO FERRARI | ISONZO | PARCO EDILCARANI | GIORDANI-FARNESE | CENO | FLAMINIA | GERBIDO | MONTEBELLO | GIARDINI | TIMAVO | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.00000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.0000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 |
| mean | 0.084700 | 0.114200 | 0.169900 | 0.213200 | 1.439700 | 6.163700 | 8.191700 | 13.288900 | 13.383800 | 17.298200 | 17.327300 | 17.440400 | 18.862400 | 20.159400 | 20.947400 | 21.590300 | 22.266600 | 22.494100 | 23.346300 | 23.748900 | 24.173500 | 24.189900 | 24.849800 | 28.25950 | 28.606600 | 29.147100 | 29.159600 | 29.453100 | 31.460200 | 32.663700 | 33.974300 | 35.803000 | 36.122500 | 37.146600 | 37.261200 | 37.404800 | 37.728800 | 37.733700 | 40.380200 | 43.072200 | 43.647400 | 44.093500 | 45.711600 | 49.9224 | 50.438500 | 54.714200 | 55.854200 | 59.622000 | 67.054000 |
| std | 0.336656 | 0.393921 | 0.477972 | 0.547335 | 1.874117 | 4.538299 | 5.566943 | 7.712067 | 7.559802 | 10.245837 | 9.490173 | 8.649215 | 10.746209 | 10.771044 | 10.750106 | 11.560822 | 11.529198 | 11.233939 | 12.010604 | 10.714884 | 10.759757 | 11.385359 | 11.942408 | 13.48826 | 12.895397 | 11.863336 | 13.050922 | 13.064121 | 14.020609 | 13.437249 | 14.321032 | 12.836957 | 15.052965 | 14.834585 | 14.079631 | 14.959115 | 14.947849 | 15.275121 | 14.064097 | 15.689723 | 15.918468 | 14.578107 | 16.045989 | 17.6247 | 18.282299 | 19.285744 | 17.229673 | 17.970203 | 20.824045 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 2.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 0.000000 | 2.000000 | 2.000000 | 2.000000 | 1.000000 | 2.000000 | 2.000000 | 4.000000 | 2.000000 | 6.000000 | 4.000000 | 5.000000 | 4.000000 | 4.000000 | 5.0000 | 6.000000 | 6.000000 | 9.000000 | 9.000000 | 14.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 3.000000 | 4.000000 | 8.000000 | 8.000000 | 10.000000 | 10.000000 | 11.000000 | 11.000000 | 12.000000 | 13.000000 | 13.000000 | 14.000000 | 14.000000 | 15.000000 | 16.000000 | 16.000000 | 16.000000 | 16.000000 | 18.00000 | 19.000000 | 21.000000 | 20.000000 | 20.000000 | 21.000000 | 23.000000 | 24.000000 | 26.000000 | 25.000000 | 27.000000 | 27.000000 | 27.000000 | 27.000000 | 27.000000 | 30.000000 | 32.000000 | 32.000000 | 34.000000 | 35.000000 | 37.0000 | 38.000000 | 41.000000 | 43.000000 | 47.000000 | 52.000000 |
| 50% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 5.000000 | 7.000000 | 12.000000 | 12.000000 | 15.000000 | 16.000000 | 16.000000 | 17.000000 | 18.000000 | 19.000000 | 20.000000 | 20.000000 | 21.000000 | 22.000000 | 22.000000 | 23.000000 | 23.000000 | 23.000000 | 26.00000 | 27.000000 | 28.000000 | 27.000000 | 28.000000 | 30.000000 | 31.000000 | 32.000000 | 35.000000 | 34.000000 | 35.000000 | 36.000000 | 36.000000 | 36.000000 | 36.000000 | 39.000000 | 41.000000 | 42.000000 | 43.000000 | 44.000000 | 48.0000 | 49.000000 | 53.000000 | 54.000000 | 58.000000 | 65.000000 |
| 75% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 8.000000 | 11.000000 | 17.000000 | 17.000000 | 23.000000 | 23.000000 | 22.000000 | 25.000000 | 26.000000 | 27.000000 | 28.000000 | 29.000000 | 29.000000 | 30.000000 | 30.000000 | 30.000000 | 31.000000 | 32.000000 | 35.25000 | 36.000000 | 36.000000 | 37.000000 | 37.000000 | 39.000000 | 41.000000 | 42.000000 | 44.000000 | 45.000000 | 46.000000 | 46.000000 | 46.000000 | 46.000000 | 47.000000 | 49.000000 | 53.000000 | 53.000000 | 53.000000 | 55.000000 | 60.0000 | 61.000000 | 66.000000 | 66.000000 | 71.000000 | 80.000000 |
| max | 5.000000 | 4.000000 | 6.000000 | 6.000000 | 22.000000 | 51.000000 | 48.000000 | 83.000000 | 61.000000 | 96.000000 | 86.000000 | 83.000000 | 101.000000 | 98.000000 | 87.000000 | 114.000000 | 121.000000 | 103.000000 | 101.000000 | 90.000000 | 87.000000 | 95.000000 | 98.000000 | 124.00000 | 105.000000 | 97.000000 | 110.000000 | 107.000000 | 136.000000 | 104.000000 | 156.000000 | 100.000000 | 120.000000 | 127.000000 | 113.000000 | 119.000000 | 139.000000 | 131.000000 | 119.000000 | 131.000000 | 145.000000 | 115.000000 | 127.000000 | 148.0000 | 155.000000 | 153.000000 | 147.000000 | 142.000000 | 174.000000 |
pd.set_option('display.max_columns', 500)
aux_results = pd.DataFrame(ritorno['inference_data'].posterior.is_over_daily_limit.values.reshape((aux_shape[0]*aux_shape[1],aux_shape[2])), columns=df.columns).describe()
aux_results.sort_values('mean', axis=1, ascending=True)
| CASTELLUCCIO | FEBBIO | CORTE BRUGNATELLA | SAVIGNANO DI RIGO | SAN LEO | VERUCCHIO | BADIA | MARECCHIA | GIARDINI MARGHERITA | DELTA CERVIA | LUGAGNANO | SAN PIETRO CAPOFIUME | PARCO BERTOZZI | SAN LAZZARO | PARCO RESISTENZA | GHERARDI | FRANCHINI-ANGELONI | VIA CHIARINI | DE AMICIS | PORTA SAN FELICE | BESENZONE | GAVELLO | SAVIGNANO | CAORLE | VILLA FULVIA | CASTELLARANO | ROMA | PARCO MONTECUCCO | CENTO | BOGOLESE | ZALAMELLA | MALCANTONE | SAN FRANCESCO | REMESINA | SARAGAT | PARADIGNA | S. LAZZARO | S. ROCCO | CITTADELLA | PARCO FERRARI | ISONZO | PARCO EDILCARANI | GIORDANI-FARNESE | FLAMINIA | CENO | GERBIDO | MONTEBELLO | GIARDINI | TIMAVO | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.00000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.00000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.00000 | 10000.00000 | 10000.000000 |
| mean | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000300 | 0.001200 | 0.012800 | 0.013900 | 0.037000 | 0.047100 | 0.055100 | 0.076000 | 0.088900 | 0.097300 | 0.114800 | 0.122900 | 0.125400 | 0.135300 | 0.141800 | 0.147800 | 0.149200 | 0.168800 | 0.250000 | 0.259400 | 0.267000 | 0.276000 | 0.28100 | 0.336400 | 0.371300 | 0.407700 | 0.467200 | 0.474200 | 0.498600 | 0.505300 | 0.508500 | 0.512200 | 0.512800 | 0.603700 | 0.660700 | 0.67090 | 0.703400 | 0.724600 | 0.788600 | 0.790400 | 0.842800 | 0.89440 | 0.92440 | 0.950400 |
| std | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.017319 | 0.034622 | 0.112416 | 0.117082 | 0.188771 | 0.211863 | 0.228187 | 0.265011 | 0.284614 | 0.296381 | 0.318796 | 0.328339 | 0.331188 | 0.342061 | 0.348862 | 0.354919 | 0.356303 | 0.374594 | 0.433034 | 0.438327 | 0.442414 | 0.447039 | 0.44951 | 0.472501 | 0.483177 | 0.491431 | 0.498948 | 0.499359 | 0.500023 | 0.499997 | 0.499953 | 0.499876 | 0.499861 | 0.489153 | 0.473495 | 0.46991 | 0.456781 | 0.446738 | 0.408322 | 0.407043 | 0.364007 | 0.30734 | 0.26437 | 0.217128 |
| min | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.00000 | 0.000000 |
| 25% | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.00000 | 1.000000 |
| 50% | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.00000 | 1.000000 |
| 75% | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.250000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.00000 | 1.000000 |
| max | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.00000 | 1.00000 | 1.000000 |
aux_results.loc['mean',:].plot(kind='barh', figsize=(15,15), xticks=np.linspace(0,1,11))
#res = plt.xticks(rotation = 30)
<AxesSubplot: >
pd.set_option('display.max_columns', 500)
aux_results = pd.DataFrame(ritorno['inference_data'].posterior.is_over_annual_limit.values.reshape((aux_shape[0]*aux_shape[1],aux_shape[2])), columns=df.columns).describe()
aux_results.sort_values('mean', axis=1, ascending=True)
| CASTELLUCCIO | PARCO EDILCARANI | DELTA CERVIA | BESENZONE | PORTA SAN FELICE | VILLA FULVIA | GHERARDI | PARCO MONTECUCCO | BOGOLESE | SARAGAT | PARADIGNA | REMESINA | ISONZO | GIORDANI-FARNESE | CITTADELLA | SAN FRANCESCO | CASTELLARANO | ROMA | CAORLE | LUGAGNANO | FEBBIO | CORTE BRUGNATELLA | SAVIGNANO DI RIGO | SAN LEO | VERUCCHIO | BADIA | GAVELLO | PARCO RESISTENZA | PARCO BERTOZZI | GIARDINI MARGHERITA | MARECCHIA | SAN LAZZARO | DE AMICIS | SAN PIETRO CAPOFIUME | VIA CHIARINI | FRANCHINI-ANGELONI | SAVIGNANO | MALCANTONE | S. ROCCO | S. LAZZARO | ZALAMELLA | CENTO | PARCO FERRARI | GIARDINI | MONTEBELLO | CENO | FLAMINIA | GERBIDO | TIMAVO | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0 | 10000.0000 | 10000.0000 | 10000.0000 | 10000.0000 | 10000.0000 | 10000.0000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 10000.000000 |
| mean | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.0001 | 0.000400 | 0.000400 | 0.000600 | 0.000600 | 0.001500 | 0.007400 |
| std | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0100 | 0.0100 | 0.0100 | 0.0100 | 0.0100 | 0.0100 | 0.019997 | 0.019997 | 0.024489 | 0.024489 | 0.038703 | 0.085709 |
| min | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| max | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
aux_results.loc['mean',:].plot(kind='barh', figsize=(15,15), xticks=np.linspace(0,1,11))
#res = plt.xticks(rotation = 30)
<AxesSubplot: >
y_post_pred = ritorno['inference_data'].posterior.y_post_pred.to_numpy()
y_post_pred = y_post_pred.reshape(y_post_pred.shape[0]*y_post_pred.shape[1], y_post_pred.shape[2], y_post_pred.shape[3])
y_post_pred.shape
(10000, 365, 49)
import ipywidgets as widgets
from ipywidgets import HBox, VBox
from IPython.display import display
@widgets.interact(stazione = df.columns)
def f(stazione):
col_map = sns.light_palette((20, 75, 70), input='husl', as_cmap=True)
plt.figure(figsize=(14,8))
ax = plt.subplot(1,1,1)
idx_stazione = df.columns.to_list().index(stazione)
"""
sns.lineplot(np.transpose(y_post_pred[0:50,:,idx_stazione]))
"""
lower_lim = np.zeros(len(df.index))
upper_lim = np.zeros(len(df.index))
for i in range(len(df.index)):
lower_lim[i] = np.percentile(y_post_pred[:,i,idx_stazione],2.5)
upper_lim[i] = np.percentile(y_post_pred[:,i,idx_stazione],97.5)
# sns.lineplot(lower_lim)
# sns.lineplot(upper_lim)
for i in range(len(df.index)):
ax.add_patch(mplt.patches.Rectangle((i,lower_lim[i]),1,upper_lim[i]-lower_lim[i], fill=True, color=col_map(180)))
sns.lineplot(df[stazione])
sns.lineplot(np.mean(y_post_pred[:,:,idx_stazione], axis=0))
index = 0
for line in ax.get_lines():
if index == 0:
col_map = sns.dark_palette((230,90,65), input='husl', as_cmap=True)
line.set_c(col_map(155))
else:
col_map = sns.dark_palette((10,90,65), input='husl', as_cmap=True)
line.set_c(col_map(175))
index += 1
plt.ylim(bottom=0,top=2.5)
primo_giorno = datetime.date(2018,1,1)
date_da_segnare = []
date_da_segnare_posizioni = []
for i in range(12):
date_da_segnare.append(datetime.date(2018,i+1,1))
date_da_segnare_posizioni.append((date_da_segnare[2*i] - primo_giorno).days)
date_da_segnare.append(datetime.date(2018,i+1,15))
date_da_segnare_posizioni.append((date_da_segnare[2*i+1] - primo_giorno).days)
date_da_segnare[2*i] = date_da_segnare[2*i].isoformat()
date_da_segnare[2*i+1] = date_da_segnare[2*i+1].isoformat()
plt.xticks(date_da_segnare_posizioni,date_da_segnare,rotation=65)
plt.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=True, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
labelbottom=True)
plt.grid()
"""
ax.get_legend().remove()
col_vals = np.linspace(1,255,num=len(df.columns))
index = 0
for line in ax.get_lines():
if(index == len(ax.get_lines()) - 1):
col_map = sns.dark_palette((230,90,65), input='husl', as_cmap=True)
line.set_c(col_map(175))
continue
if(index == len(ax.get_lines()) - 2):
col_map = sns.dark_palette((120,90,65), input='husl', as_cmap=True)
line.set_c(col_map(175))
index += 1
continue
#line.set_c(col_map(int(np.round(col_vals[index]))))
line.set_c(col_map(220))
line.set_alpha(0.3)
index += 1
"""
plt.show()
interactive(children=(Dropdown(description='stazione', options=('CASTELLUCCIO', 'FEBBIO', 'CORTE BRUGNATELLA',…
def f(stazione):
col_map = sns.light_palette((20, 75, 70), input='husl', as_cmap=True)
plt.figure(figsize=(14,8))
ax = plt.subplot(1,1,1)
idx_stazione = df.columns.to_list().index(stazione)
"""
sns.lineplot(np.transpose(y_post_pred[0:50,:,idx_stazione]))
"""
lower_lim = np.zeros(len(df.index))
upper_lim = np.zeros(len(df.index))
for i in range(len(df.index)):
lower_lim[i] = np.percentile(y_post_pred[:,i,idx_stazione],2.5)
upper_lim[i] = np.percentile(y_post_pred[:,i,idx_stazione],97.5)
# sns.lineplot(lower_lim)
# sns.lineplot(upper_lim)
for i in range(len(df.index)):
ax.add_patch(mplt.patches.Rectangle((i,lower_lim[i]),1,upper_lim[i]-lower_lim[i], fill=True, color=col_map(180)))
sns.lineplot(df[stazione])
sns.lineplot(np.mean(y_post_pred[:,:,idx_stazione], axis=0))
index = 0
for line in ax.get_lines():
if index == 0:
col_map = sns.dark_palette((230,90,65), input='husl', as_cmap=True)
line.set_c(col_map(155))
else:
col_map = sns.dark_palette((10,90,65), input='husl', as_cmap=True)
line.set_c(col_map(175))
index += 1
plt.ylim(bottom=0,top=2.5)
primo_giorno = datetime.date(2018,1,1)
date_da_segnare = []
date_da_segnare_posizioni = []
for i in range(12):
date_da_segnare.append(datetime.date(2018,i+1,1))
date_da_segnare_posizioni.append((date_da_segnare[2*i] - primo_giorno).days)
date_da_segnare.append(datetime.date(2018,i+1,15))
date_da_segnare_posizioni.append((date_da_segnare[2*i+1] - primo_giorno).days)
date_da_segnare[2*i] = date_da_segnare[2*i].isoformat()
date_da_segnare[2*i+1] = date_da_segnare[2*i+1].isoformat()
plt.xticks(date_da_segnare_posizioni,date_da_segnare,rotation=65)
plt.tick_params(
axis='x', # changes apply to the x-axis
which='both', # both major and minor ticks are affected
bottom=True, # ticks along the bottom edge are off
top=False, # ticks along the top edge are off
labelbottom=True)
plt.grid()
"""
ax.get_legend().remove()
col_vals = np.linspace(1,255,num=len(df.columns))
index = 0
for line in ax.get_lines():
if(index == len(ax.get_lines()) - 1):
col_map = sns.dark_palette((230,90,65), input='husl', as_cmap=True)
line.set_c(col_map(175))
continue
if(index == len(ax.get_lines()) - 2):
col_map = sns.dark_palette((120,90,65), input='husl', as_cmap=True)
line.set_c(col_map(175))
index += 1
continue
#line.set_c(col_map(int(np.round(col_vals[index]))))
line.set_c(col_map(220))
line.set_alpha(0.3)
index += 1
"""
plt.show()
for stazione in df.columns:
f(stazione)
